Specific diagnosis of a number of human brain disorders demands extensive use of a number of imaging methods, such as computer assisted tomography (CT) and nuclear magnetic resonance imaging (MRI), recording of spontaneous and evoked electrical activity, but very often also open craniotomy to obtain a specimen from the tissue for microscopic examination. While the imaging methods and extracranial electrical recordings are non-invasive, in other words contain neither immediate patient management nor health risk, open cranial biopsies or electrical recordings intracranially pose given risks for the subjects. Both CT and MRI are capable of imaging anatomy in great detail, but they do not provide information from the type of pathology in question. Consequently, their value in neuroradiological diagnosis currently is twofold; firstly, to locate the pathology and secondly, to guide neurosurgeons in determining the site for the open biopsy procedure.
It is technically possible to record non-invasively metabolic information by water-suppressed proton nuclear magnetic resonance spectroscopy (.sup.1 H MRS) with high degree of volume-selection from human brain regions. This method has made it possible to approach brain metabolism both in healthy and diseased human subjects without immediate harms. Brain metabolites that can be visualised by .sup.1 H MRS, provides different type of information than either CT or MRI do. CT indicates absorption of X-rays in the brain tissue and MRI probes magnetic properties of water molecules and sometimes also those of fat. .sup.1 H MRS reveals specific biochemical compounds which reflect metabolic activity of both normal and abnormal brain. These include N-acetyl aspartate, creatine+phosphocreatine, choline-containing compounds, myo-inositol, scyllo inositol, glutamic acid, glutamine, lactic acid, adenine nucleotides as well as portions of mobile fat and protein moieties.
The conventional in vivo .sup.1 H MRS data processing is accomplished by means of peak integration or peak fitting of signals from the assigned metabolites. This type of data analysis, however, is prone to several technical flaws which are user-biased and therefore both biochemically and diagnostically useful information may become overruled. Also .sup.1 H MRS artefacts, such as prominent residual water signal have made previous efforts of metabolic classification according to the spectral information ambiguous. Metabolite quantification by ANN relys on simulated or empirically measured reference material and the test sample is compared with the former data set. Therefore the metabolite quantification procedure is independent of many technical difficulties such as signal intensity quantification and determination of relaxation parameters.